app.py

import gradio as gr
import torch
# from transformers import AutoModelForSequenceClassification, AutoTokenizer
from transformers import BertTokenizer, BertForSequenceClassification

# Load the model and tokenizer from Hugging Face
model_name = "crimson78/spam_classifier_models"  # Replace with your model's actual name
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(model_name)

# Define label mapping (Assuming 0 = HAM, 1 = SPAM)
LABELS = {0: "HAM", 1: "SPAM"}

# Function to classify the input text
def classify_text(message):
    inputs = tokenizer(message, return_tensors="pt", truncation=True, padding=True, max_length=512)
    with torch.no_grad():
        outputs = model(**inputs)
    logits = outputs.logits
    prediction = torch.argmax(logits, dim=-1).item()
    return LABELS[prediction]

# Gradio interface
iface = gr.Interface(
    fn=classify_text,
    inputs=gr.Textbox(label="Enter your message"),
    outputs=gr.Label(label="Classification"),
    title="Spam Classifier",
    description="Enter a message to check if it's SPAM or HAM using a fine-tuned BERT model.",
)

# Run the app
if __name__ == "__main__":
    iface.launch()